1. Field of the Invention
The present invention generally relates to the field of digital signal processing and, more specifically to an edge enhancement method and apparatus for Bayer images.
2. Description of the Related Art
Generally, three image sensors are required to respectively record three color values for each pixel to display the true color of an object. In order to reduce the circuit size and the cost of hardware, a majority of the image acquisition systems simply utilize a single image sensor overlaid by a color filter array. Consequently, they capture only one color for each pixel, so that it is required to reconstruct missing color values for each pixel. FIG. 1A is a diagram of the Bayer geometry for a color filter array.
FIG. 1B is a block diagram of a conventional image acquisition system. Referring to FIG. 1B, the image acquisition system 100 includes an image acquisition unit 101, a buffer 103, an interpolator 104, a color correction unit 105, a gamma correction unit 106, an RGB to YUV converter 107 and an edge enhancement unit 108, wherein “Y” represents a luminance signal and “UV” represents a chrominance signal.
The image acquisition system 100 uses the image acquisition unit 101 to capture image data and generate the raw Bayer image data, then stores the image data into the buffer 103. The interpolator 104 receives the raw Bayer image data output from the buffer 103, generates the missing color values for each pixel by using interpolation or other algorithms, and outputs the RGB image data. The color correction unit 105 receives the RGB image data and performs the color correction. The gamma correction unit 106 performs the gamma correction on the image data output from the color correction unit 105. The RGB to YUV converter 107 converts the RGB three color values into the YUV three color values for each pixel and then outputs the YUV image data.
The clarity of the image captured by the image acquisition unit 101 varies according to the sensitivity and the resolution of the photosites. Generally, there is an edge enhancement unit 108 embedded in the image acquisition system 100 for performing image edge enhancement and enhancing the clarity of the image.
In U.S. Pat. No. 6,192,162, Hamilton, Jr., et al. discloses a method for edge enhancement of a digital image. The method computes a luminance value using RGB three color values, or simply uses the green channel of the image to compute parameters in each orientation for each respective pixel. Depending on surrounding parameters, each pixel in the image is classified if the pixel neighborhood contains any edges in the luminance record or if the pixel neighborhood is in a “flat” region of the image. For the “un-flat” classification, an edge boost kernel is adaptively chosen based on edge orientation. If only the green channel of the image is used to determine the pixel classification, the sharpening processes amplify noise as well as image content because the green channel is not fully representative of the luminance value. Instead, if the pixel classification is to be determined by luminance values, the missing color values are initially reconstructed (or interpolated) so that each pixel contains three full color values. Then three full color values for each pixel are converted to the luminance domain for the edge classification and the edge enhancement.
In U.S. Pat. No. 6,774,943, Kao et. al. teaches a method and apparatus for edge enhancement of a digital image. An edge value is assigned to each pixel in an image in accordance with a difference in luminance between that pixel and neighboring pixels. The edge value for each pixel is scaled and then combined with the original luminance value of the pixel to provide an enhanced value for edge enhancement.
However, the two above-mentioned methods are all applied to the interpolated image data; therefore, it consumes a large amount of memory space and causes delays in timing to compute the luminance values and classification of the edge content.